Tom Brashers-Krug, Reza Shadmehr, Emanuel Todorov
Biological sensorimotor systems are not static maps that transform input (sensory information) into output (motor behavior). Evi(cid:173) dence from many lines of research suggests that their representa(cid:173) tions are plastic, experience-dependent entities. While this plastic(cid:173) ity is essential for flexible behavior, it presents the nervous system with difficult organizational challenges. If the sensorimotor system adapts itself to perform well under one set of circumstances, will it then perform poorly when placed in an environment with different demands (negative transfer)? Will a later experience-dependent change undo the benefits of previous learning (catastrophic inter(cid:173) ference)? We explore the first question in a separate paper in this volume (Shadmehr et al. 1995). Here we present psychophysical and computational results that explore the question of catastrophic interference in the context of a dynamic motor learning task. Un(cid:173) der some conditions, subjects show evidence of catastrophic inter(cid:173) ference. Under other conditions, however, subjects appear to be immune to its effects. These results suggest that motor learning can undergo a process of consolidation. Modular neural networks are well suited for the demands of learning multiple input/output mappings. By incorporating the notion of fast- and slow-changing connections into a modular architecture, we were able to account for the psychophysical results.
Tom Brashers-Krug, Reza Shadmelzr, Emanuel Todorov